class_docstr: The soft target CrossEntropy from timm.
  https://github.com/rwightman/pytorch-image-models/blob/e4360e6125bb0bb4279785810c8eb33b40af3ebd/timm/loss/cross_entropy.py
  It works under the mixup.
  It can calculate the crossentropy of input and label with one-hot.
code: def forward(
      self, input: torch.Tensor, target: torch.Tensor
  ) -> torch.Tensor:
      loss = torch.sum(-target * F.log_softmax(input, dim=-1), dim=-1)
      return loss.mean()
  
imports: import logging
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the SoftTargetCrossEntropy obj's underlying member method code with example usage: obj.forward(input, target) and returns: loss